It is challenging to analyze and compare the layer representations of deep networks, because their features are high-dimensional and with different dimensions. Centered kernel alignment (CKA) [1]} is defined to address this challenge, and enables quantitative comparisons of feature representations within and across networks. Given two inputs of \({ X} \in \mathbb {R}^{N\times D_1}\)  and \({ Y} \in \mathbb {R}^{N\times D_2}\) , where \(N\)  denotes number of examples and \(D_1\)  and \(D_2\)  denote the dimension. Then the Gram matrices are computed as \(K=X X^T\)  and \(L=Y Y^T\) . CKA is then defined as
\(\text{CKA}(K, L) = \frac{\text{HSIC}(K, L)}{\sqrt{\text{HSIC}(K, K) \text{HSIC}(L, L)}},\) 
